Selection and Detection of Network Intrusion Feature Based on BPSO-SVM
Xingyu Wang · Jisuanji gongcheng · 2006
In the proposed algorithm,every particle in the swarm stands for a selected subset of features.The fitness of particle is defined as the correct classification percentage by SVM using a training set whose patterns are represented using only the selected subset of features.Thus through particle swarm optimization to achieve intrusion feature selection and classification.A probabilistic mutation of BPSO is adopted to avoid local optimal and a tabu search table is used to enlarge particle swarm’s search space and avoid repeated computation.The results of experiment demonstrate that applying a hybrid of BPSO-SVM in intrusion detection System can be an effective way for feature selection and detecting intrusions via using the data sets of KDD cup 99.